Global methods for genomic analysis, such as karyotyping, determination of ploidy, and more recently comparative genomic hybridizaton (CGH) (Feder et al., 1998, Cancer Genet. Cytogenet, 102:25-31; Gebhart et al., 1998, Int. J. Oncol. 12:1151-1155; Larramendy et al., 1997, Am. J. Pathol. 151:1153-1161; Lu et al., 1997, Genes Chromosomes Cancer 20:275-281) have provided useful insights into the pathophysiology of cancer and other diseases or conditions with a genetic component, and in some instances have aided diagnosis, prognosis and selection of treatment. However, those methods do not afford a level of resolution of greater than can be achieved by standard microscopy, or about 5-10 megabases. Moreover, while many particular genes that are prone to mutation can be used as probes to interrogate the genome in very specific ways (e.g., Ford et al., 1998, Am. J. Hum. Genet. 62:676-689; Gebhart et al., 1998, Int. J. Oncol. 12:1151-1155; Hacia et al., 1996, Nat. Genet. 14:441-447), this one-by-one query is an inefficient and incomplete method for genetically typing cells.
The microarray or “chip” technology has made it possible to contemplate obtaining a high resolution global image of genetic changes in cells. Two general approaches can be conceived. One is to profile the expression pattern of the cell using microarrays of cDNA probes (e.g., DeRisi et al., 1996, Nat. Genet. 14:457-460). The second approach is to examine changes in the cancer genome itself, which has several advantages over the expression profiling approach. First, DNA is more stable than RNA, and can be obtained from poorly handled tissues, and even from fixed and archived biopsies. Second, the genetic changes that occur in the cancer cell, if their cytogenetic location can be sufficiently resolved, can be correlated with known genes as the databases of positionally mapped cDNAs mature. Thus, the information derived from such an analysis is not likely to become obsolete. The nature and number of genetic changes can provide clues to the history of the cancer cell. Third, a high resolution genomic analysis may lead to the discovery of new genes involved in the etiology of the disease or disorder of interest.
DNA-based methods for global genome analysis, for example, measuring changes in copy number, include fluorescent in situ hybridization (FISH), the BAC array, and cDNA arrays. FISH has been used clinically to evaluate amplification at the ErbB-2 locus in breast cancer (Tkachuk et al., 1990, Science 250:559-562; Bartlett and Mallon, 2003, J. Pathology 199:418-423), but FISH relies on having a probe that hybridizes to a single locus that may be important in selecting cancer therapy. A major disadvantage of the BAC array and the cDNA array methods is low resolution.
WO0183822 and WO00923256 disclose certain methods and compositions to solve the problems associated with using microarrays to conduct DNA-based global genome analysis, particularly of a genome based on DNA extracted from scant, nonrenewable sources such as tumor or cancer tissue samples. These patent applications relate to a technology termed Representational Oligonucleotide Microarray Analysis (ROMA), a powerful tool for detecting genetic rearrangements such as amplifications, deletions, and sites of breakage in cancer and normal genomes, by comparative genomic hybridization (CGH).
These genomic profiling methods provide useful tools to detect and identify chromosomal alterations, which are hallmarks of cancer cells as well as of other diseases such as certain degenerative and neurobehavioral diseases. See e.g., Gericke, Med. Hypotheses. 2006; 66(2):276-285, Epub 2005 Sep. 22. In humans, non-cancerous cells contain two complete copies of each of 22 chromosomes plus to two X chromosomes in females, or one X and one Y chromosomes in males. Cancer cells exhibit a wide range of genomic rearrangements, including deletion (e.g., lowering copy number from 2 to 1 or 0), duplication (e.g., raising copy number from 2 to 3 or 4) of DNA segments, amplification of DNA segments up to 60 copies, and duplication or triplication of the entire set of chromosomes (i.e., aneuploidy). Comparing genomic profiles between cancer cells and normal cells from a particular patient, or between cancer cells from samples from different patients with different disease progression states and who have undergone different treatments would provide correlations between particular genetic alterations with particular cancer or patient traits. Such correlations would be useful in cancer diagnosis, cancer patient stratification for any given therapy, and predicting clinical outcome based on a patient's genomic profile. Therefore, a need exists for new methods that would make such correlation feasible.
Many diseases and conditions involve alterations at the chromosomal level. Many cancers, for example, involve genomic alterations. As cancers evolve, their genomes undergo many alterations, including point mutations, rearrangements, deletions and amplifications, which presumably alter the ability of the cancer cell to proliferate, survive and spread in the host (Balmain et al., 2003; DePinho and Polyak, 2004). Other diseases that may involve genomic rearrangements include, but are not limited to, autism and schizophrenia. Diseases that involve certain genetic predisposition may also involve genomic rearrangements, such as obesity. For other diseases (such as certain degenerative diseases and neurobehavioral diseases), genomic changes or rearrangements are presumably deleterious to cell growth and/or survival.
An understanding of these chromosomal level alterations or genomic changes will allow the design of more rational therapies and, by providing precise diagnostic criteria, allow fitting the correct therapy to each patient according to need. For example, primary breast cancers in particular exhibit a wide range of outcomes and degrees of benefit from systemic therapies, which are incompletely predicted by conventional clinical and clinico-pathological features. This is especially apparent in the case of small primaries without axillary lymph node involvement, which usually have a good prognosis but are sometimes associated with eventual metastatic dissemination and inevitable lethality.
Breast tumors, for example, have long been known to suffer multiple genomic rearrangements during their development and thus it is reasonable to hypothesize that clinical heterogeneity may be caused by the existence of genetically distinct subgroups. One common approach to the molecular characterization of breast cancer has been “expression profiling”, measuring the entire transciptome by microarray hybridization. Expression profiling has been very effective at revealing phenotypic subtypes of breast cancer and clinically useful diagnostic patterns of gene expression in tumors (Ahr et al., 2002; van't Veer et al., 2002; Paik et al., 2004; Perou et al., 2000; Sorlie et al., 2001; Sotiriou, 2003). Expression profiling does not look directly at underlying genetic changes, and its dependence on RNA, a fragile molecule, creates some problems in standardization and cross validation of microarray platforms. Moreover, variation in the physiological context of the cancer within the host, such as the proportion of normal stroma and the degree of inflammatory response, or the degree of hypoxia, as well as methods used for extraction and preservation of sample, are all potentially confounding factors (Eden et al., 2004).
Direct analysis of the tumor genome provides an alternative and perhaps, complementary, means of comparing breast tumors by revealing the genetic events accumulated during tumor progression. A long-term genomic study has been initiated and conducted for clinically well-defined sets of breast cancer patients with ROMA (Lucito et al., 2003). ROMA is based on the principle that noise in microarray hybridization can be significantly reduced by reducing the complexity of the labeled DNA target in the hybridization mix. In its current configuration ROMA uses a “representation” of the genome created by PCR amplification of the smallest fragments of a BglII restriction digest. The representation contains less than 3% the complexity of the normal human genome and is specifically matched with a unique microarray containing over 83,000 oligonucleotide probes designed to pair with the amplified fragments. Coupled with an efficient edge-detection or segmentation algorithm, ROMA yields highly precise profiles of even closely spaced amplicons and deletions. Currently, ROMA is capable of detecting the breakpoints of chromosomal events at a resolution of 50 kb.
ROMA is a powerful tool for genomic profiling. Nevertheless, there remains a need for improvements in analysis of data obtained by ROMA as well as by other methods that represent segments of the genome. With such improved analytical tools and methods, one will be better able to manipulate high resolution genomic data analysis and apply it to the clinical, therapeutic setting. Such improved analytical tools and methods will also continue to improve our ability to track genetic events and to understand their effects on the etiology of disease.
The first global studies capable of resolving deletions and amplifications combined comparative genomic hybridization (CGH) and cytogenetics (Kallioniemi et al., 1992a; Kallioniemi et al., 1992b; Kallioniemi et al., 1992c) and this approach has been applied to breast tumors (Kallioniemi et al., 1994; Tirkkonen et al., 1998; Ried et al., 1997). Subsequently, microarray methods employing CGH have increased resolution and reproducibility, and improved throughput (Albertson, 2003; Lage et al., 2003; Ried et al., 1995; Pollack et al., 2002). These published microarray studies have largely validated the results of cytogenetic CGH, but have not had sufficient resolution to significantly improve our knowledge of the role of genetic events in the etiology of disease, nor assist in the treatment of the patient. On the other hand, knowledge of specific genetic events, like amplification of ERBB2, as studied by FISH or Q-PCR, has been clinically useful (van, V et al., 1987; Slamon et al., 1989; Menard et al., 2001). ROMA provides an extra measure of resolution in genomic analysis that might be useful in clinical evaluation, as well as delineating loci important in disease evolution.